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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

769
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter

Fırat Hardalaç1, Haad Akmal2,3, Kubilay Ayturan1

  • 1Department of Electrical Electronics Engineering, Gazi University, 06560, Ankara, Türkiye.

Interdisciplinary Sciences, Computational Life Sciences
|October 5, 2024
PubMed
Summary

This study introduces a machine learning strategy to analyze Cardiotocography (CTG) for fetal health assessment. The model accurately classifies fetal states, improving diagnostic support for pregnancies.

Keywords:
CardiotocographyClassificationFeature eliminationFetal wellbeingHyperparameter optimizationMachine learning

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Area of Science:

  • Obstetrics and Gynecology
  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Cardiotocography (CTG) is crucial for monitoring fetal well-being during late pregnancy and labor.
  • Assessing fetal heart rate (FHR) patterns in response to uterine contractions (UC) helps identify potential fetal distress.
  • Accurate interpretation of CTG is vital for timely intervention and improved perinatal outcomes.

Purpose of the Study:

  • To develop and evaluate a machine learning strategy for classifying fetal states (Normal, Suspect, Pathological) using CTG data.
  • To enhance the accuracy of fetal state classification through feature reduction and hyperparameter optimization.
  • To explore the potential of this model as a decision support tool for pregnancy management and remote fetal monitoring.

Main Methods:

  • A pragmatic machine learning approach was applied to a public dataset of 2126 CTG recordings.
  • Feature reduction and hyperparameter optimization techniques were employed to refine classification models.
  • The proposed model was evaluated using standard classifiers, including Random Forest, and validated on a second dataset of 552 CTG signals.

Main Results:

  • The machine learning strategy significantly improved classifier accuracy for fetal state classification.
  • The Random Forest classifier achieved a peak accuracy of 97.20% on the primary dataset.
  • The model demonstrated high predictive performance, correctly identifying 100% of pathological and 98.8% of normal fetal cases.
  • Validation on a separate dataset yielded an accuracy of 97.34%.

Conclusions:

  • The proposed machine learning model offers a robust and accurate method for interpreting Cardiotocography data.
  • This technology can serve as an effective decision support tool for obstetricians, aiding in the management of high-risk pregnancies.
  • Integration with telemedicine platforms could enable remote fetal monitoring, enhancing accessibility and care for expectant mothers.